posted on 2024-02-17, 14:06authored byGhaith Mqawass, Petr Popov
Predicting the binding
affinity of protein–ligand complexes
is crucial for computer-aided drug discovery (CADD) and the identification
of potential drug candidates. The deep learning-based scoring functions
have emerged as promising predictors of binding constants. Building
on recent advancements in graph neural networks, we present graphLambda
for protein–ligand binding affinity prediction, which utilizes
graph convolutional, attention, and isomorphism blocks to enhance
the predictive capabilities. The graphLambda model exhibits superior
performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different
types of train-validation set partitions. The development of graphLambda
underscores the potential of graph neural networks in advancing binding
affinity prediction models, contributing to more effective CADD methodologies.